1. Field of the Invention
The present invention relates to a texture description method of an image, and more particularly to a texture description method for transforming an image of a time domain into an image of a frequency domain and extracting texture features by Gabor filtering. Also, the present invention relates to a texture-based method of retrieving images indexed by the texture description method.
2. Description of the Related Art
Texture information and its application as an indication of important visual features of an image, have been studied for a long time. The texture information of an image is used as a low level descriptor for content-based indexing and abstracting an image or video data. Also, the texture information of the image is important in retrieving a specific photo of a digital photo album, or content-based retrieving in a tile or a textile database.
Presently, feature values are calculated in a time domain or a frequency domain in order to extract texture features of an image. In particular, a method of texture feature extraction in a frequency domain is known to be suitable for describing the texture features of images of a wide variety of forms.
A thesis on this method, entitled “Texture Features of Browsing and Retrieval of Image Data”, by B. S. Manjunath and W. Y. Ma, published on IEEE Transaction on Pattern Analysis and Machine Intelligence, Volume 18, No. 8, on August 1996, describes a method for calculating feature vectors by extracting from the image obtained after Gabor filtering in the frequency domain, the mean and the variance of each channel as feature values of the texture of an image.
However, the image texture description method using the conventional Gabor filtering has problems. First, it takes a long time for calculation by performing the Gabor filtering of an image in a signal domain. Second, it is difficult to obtain enough information because the density of frequency samples of an image is low in the case where the texture information is extracted using the Gabor filter having a narrow pass band in a low frequency domain due to the use of an orthogonal frequency domain. Third, the size of data needed to describe features is great because both the mean and variance of an image brightness value are used as the texture features of the image.